
"""simple docstring"""


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)






def  lowercase						(	lowerCAmelCase__						):
			if hor == 128:
						lowerCamelCase_					=  ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						lowerCamelCase_					=  (32, 128, 256)
						lowerCamelCase_					=  ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						lowerCamelCase_					=  ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						lowerCamelCase_					=  (32, 64, 128, 256)
						lowerCamelCase_					=  ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			lowerCamelCase_					=  torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"						)
			lowerCamelCase_					=  model.state_dict()
			lowerCamelCase_					=  {
			    '''down_block_types''': down_block_types,
			    '''block_out_channels''': block_out_channels,
			    '''up_block_types''': up_block_types,
			    '''layers_per_block''': 1,
			    '''use_timestep_embedding''': True,
			    '''out_block_type''': '''OutConv1DBlock''',
			    '''norm_num_groups''': 8,
			    '''downsample_each_block''': False,
			    '''in_channels''': 14,
			    '''out_channels''': 14,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''sample_size''': 65_536,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			lowerCamelCase_					=  UNetaDModel(**lowerCAmelCase__						)
			print(f"length of state dict: {len(state_dict.keys()						)}"						)
			print(f"length of value function dict: {len(hf_value_function.state_dict().keys()						)}"						)
			lowerCamelCase_					=  dict(zip(model.state_dict().keys()	,hf_value_function.state_dict().keys()						)						)
			for k, v in mapping.items():
						lowerCamelCase_					=  state_dict.pop(lowerCAmelCase__						)
			hf_value_function.load_state_dict(lowerCAmelCase__						)

			torch.save(hf_value_function.state_dict()	,f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"						)
			with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"	,'''w'''						) as f:
						json.dump(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	):
			lowerCamelCase_					=  {
			    '''in_channels''': 14,
			    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
			    '''up_block_types''': (),
			    '''out_block_type''': '''ValueFunction''',
			    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
			    '''block_out_channels''': (32, 64, 128, 256),
			    '''layers_per_block''': 1,
			    '''downsample_each_block''': True,
			    '''sample_size''': 65_536,
			    '''out_channels''': 14,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''use_timestep_embedding''': True,
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''norm_num_groups''': 8,
			    '''act_fn''': '''mish''',
			}

			lowerCamelCase_					=  torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
			lowerCamelCase_					=  model
			lowerCamelCase_					=  UNetaDModel(**lowerCAmelCase__						)
			print(f"length of state dict: {len(state_dict.keys()						)}"						)
			print(f"length of value function dict: {len(hf_value_function.state_dict().keys()						)}"						)

			lowerCamelCase_					=  dict(zip(state_dict.keys()	,hf_value_function.state_dict().keys()						)						)
			for k, v in mapping.items():
						lowerCamelCase_					=  state_dict.pop(lowerCAmelCase__						)

			hf_value_function.load_state_dict(lowerCAmelCase__						)

			torch.save(hf_value_function.state_dict()	,'''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'''						)
			with open('''hub/hopper-medium-v2/value_function/config.json'''	,'''w'''						) as f:
						json.dump(lowerCAmelCase__	,lowerCAmelCase__						)


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()




"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   "list[int]":




						'''simple docstring'''



						if upper_limit < 0:
												raise ValueError("Limit for the Catalan sequence must be ≥ 0"							)

						lowerCamelCase__   =[0] * (upper_limit + 1)

						# Base case: C(0) = C(1) = 1
						lowerCamelCase__   =1
						if upper_limit > 0:
												lowerCamelCase__   =1

						# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
						for i in range(2  ,       upper_limit + 1							):
												for j in range(__lowerCAmelCase							):
																		catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

						return catalan_list


if __name__ == "__main__":
	print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
	print('\n*** Enter -1 at any time to quit ***')
	print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
	try:
		while True:
			a				=int(input().strip())
			if N < 0:
				print('\n********* Goodbye!! ************')
				break
			else:
				print(F'''The Catalan numbers from 0 through {N} are:''')
				print(catalan_numbers(N))
				print('Try another upper limit for the sequence: ', end='')
	except (NameError, ValueError):
		print('\n********* Invalid input, goodbye! ************\n')

	import doctest

	doctest.testmod()


